Network provisioning processes enable a network to determine the location and size of different network resources available for long-term deployment, typically on an order of several months or years. Such provisioning processes are typically based solely on projected traffic demands within a network and agreed traffic exchanges across other networks.
Traditionally, network provisioning processes have been based on underlying traffic-characterization models, traffic-flow models and performance models. Traditional provisioning processes work well in a telephone network where traffic loads are predictable. In telephone networks, traffic can be characterized with a reasonable accuracy. Further, performance models that relate performance to traffic loads and network resources are both accurate and tractable with respect to telephone networks.
However, conditions applicable to telephone networks do not necessarily apply to fast-evolving data networks. Accurate traffic characterization at microscopic and macroscopic levels is not feasible in rapidly evolving data networks. Microscopic characterization refers to a parametric description of each traffic stream, whereas macroscopic characterization is concerned with spatial distribution of traffic. Microscopic-level characterization is difficult due to rapidly changing nature of traffic and macroscopic-level characterization is difficult due to changing network services.
Elaborate traffic characterization models and ensuing provisioning models are generally applicable over only relatively short timescales. It would take researchers years to develop and refine such models only to discover, before the exercise is complete, that the models are obsolete. One example is the development of the tedious Markov Modulated Poisson Process, which is based on assumptions that have since been deemed to be inadequate. Another extreme is a self-similar-traffic model, which may yield an unduly pessimistic estimation of a network performance. In addition to inadequacies of these models, traffic parameters required to implement mathematical models are difficult to determine. Data traffic is a rapidly moving target and its composition changes with technology. Higher access capacity, new applications, new protocols, new tariff systems, etc., have a significant effect on traffic characteristics at both microscopic and macroscopic levels.
The use of precise microscopic models, even if they are attainable and tractable, cannot be justified, given the rapid change of the traffic nature. Instead, a simplified traffic model may possibly be used as long as the values of the characterizing parameters are updated frequently. A simplified traffic model also leads to a simplified microscopic provisioning model that facilitates computation of traffic-flow across a network. The use of frequently updated simplified models, however, requires traffic monitoring, at least at each source node and frequent execution of provisioning models. The network performance, however defined, must still be measured in order to validate the provisioning model. Furthermore, considering the unavailability of a satisfactory traffic model, it is difficult to determine what to measure and how to extract parameters from measurements. Moreover, current solutions require the use of both traffic models and traffic monitoring, which would require excessive computing time. Thus, relevant current data on which to base provisioning decisions may not be easily obtained.
Currently, computer-aided tools are used for network provisioning. Core nodes provided in such networks may include cross-connectors, optical and/or electronic, which may be configured on a semi-permanent basis. Some proposals (e.g., ITU-T's G.8080/Y.1304 Automatically Switched Transport Network (ASTN)) introduce dynamic configurations, but still require configuration requirements to be determined through manual intervention. Therefore, there is still a need to automatically predict such configuration requirements through learning based on measurements in the network.
In view of the foregoing, it would be desirable to provide a technique for autonomous network provisioning which overcomes the above-described inadequacies and shortcomings. More particularly, it would be desirable to provide a technique for autonomous network provisioning in an efficient and cost effective manner.